Improving dynamic tomography, through Maximum a posteriori estimation | Zendy

Glenn R. Myers | Zendy; Matthew Geleta | Zendy; Andrew Kingston | Zendy; Benoît Recur | Zendy; Adrian Sheppard | Zendy

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Improving dynamic tomography, through Maximum a posteriori estimation

Author(s) -

Glenn R. Myers,

Matthew Geleta,

Andrew Kingston,

Benoît Recur,

Adrian Sheppard

Publication year - 2014

Publication title -

proceedings of spie, the international society for optical engineering/proceedings of spie

Language(s) - English

Resource type - Conference proceedings

SCImago Journal Rank - 0.192

H-Index - 176

eISSN - 1996-756X

pISSN - 0277-786X

DOI - 10.1117/12.2061604

Subject(s) - maximum a posteriori estimation , a priori and a posteriori , tomography , computer science , estimation , maximum likelihood , algorithm , artificial intelligence , mathematics , statistics , medicine , engineering , radiology , philosophy , epistemology , systems engineering

Direct study of pore-scale fluid displacements, and other dynamic (i.e. time-dependent) processes is not feasible with conventional X-ray micro computed tomography (μCT). We have previously verified that a priori knowledge of the underlying physics can be used to conduct high-resolution, time-resolved imaging of continuous, complex processes, at existing X-ray μCT facilities. In this paper we present a maximum a posteriori (MAP) model of the dynamic tomography problem, which allows us to easily adapt and generalise our previous dynamic μCT approach to systems with more complex underlying physics.

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